MODELING CONTENT CREATOR INCENTIVES ON ALGORITHM-CURATED PLATFORMS

Abstract

Content creators compete for user attention. Their reach crucially depends on algorithmic choices made by developers on online platforms. To maximize exposure, many creators adapt strategically, as evidenced by examples like the sprawling search engine optimization industry. This begets competition for the finite user attention pool. We formalize these dynamics in what we call an exposure game, a model of incentives induced by algorithms, including modern factorization and (deep) two-tower architectures. We prove that seemingly innocuous algorithmic choices-e.g., non-negative vs. unconstrained factorization-significantly affect the existence and character of (Nash) equilibria in exposure games. We proffer use of creator behavior models, like exposure games, for an (ex-ante) predeployment audit. Such an audit can identify misalignment between desirable and incentivized content, and thus complement post-hoc measures like content filtering and moderation. To this end, we propose tools for numerically finding equilibria in exposure games, and illustrate results of an audit on the MovieLens and LastFM datasets. Among else, we find that the strategically produced content exhibits strong dependence between algorithmic exploration and content diversity, and between model expressivity and bias towards gender-based user and creator groups.

1. INTRODUCTION

In 2018, Jonah Peretti (CEO, Buzzfeed) raised alarm when a Facebook main feed update started boosting junk and divisive content (Hagey & Horwitz, 2021) . In Poland, the same update caused an uptick in negative political messaging (Hagey & Horwitz, 2021) . Tailoring content to algorithms is not unique to social media. For example, some search engine optimization (SEO) professionals specialize on managing impacts of Google Search updates (Marentis, 2014; Dennis, 2016; Shahzad et al., 2020; Patil et al., 2021; Goodwin, 2021) . While motivations for adapting content range from economic to socio-political, they often translate into the same operative goal: exposure maximization. We study how algorithms affect exposuremaximizing content creators. We propose a novel incentive-based behavior model called an exposure game, where producers compete for a finite user attention pool by crafting content ranked highly by a given algorithm (Section 1.1). When producers act strategically, a steady state-Nash equilibrium (NE)-may be reached, with no one able to unilaterally improve their exposure (utility). The content produced in a NE can thus be interpreted as what the algorithm implicitly incentivizes. We focus on algorithms which model user preferences as an inner product of d-dimensional user and item embeddings, and rank items by the estimated preference. Section 2 presents theoretical results on the NE induced by these algorithms. We identify cases where algorithmic changes seemingly unconnected to producer incentives-e.g., switching from non-negative to unconstrained embeddings-determine whether there are zero, one, or multiple NE. The character of NE is also



Figure 1: Exposure game. Items s i ∈ S d-1 placed to maximize exposure to consumers c ∼ P c .

